Author name cluster

Lianghua He

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8 papers

2 author rows

AAAI Conference 2025 Conference Paper

AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic Images

Yihang Liu
Lianghua He
Ying Wen
Longzhen Yang
Hongzhou Chen

Current self-supervised methods, such as contrastive learning, predominantly focus on global discrimination, neglecting the critical fine-grained anatomical details required for accurate radiographic analysis. To address this challenge, we propose the Anatomy-driven self-supervised framework for enhancing Fine-grained Representation in radiographic image analysis (AFiRe). The core idea of AFiRe is to align the anatomical consistency with the unique token-processing characteristics of Vision Transformer. Specifically, AFiRe synergistically performs two self-supervised schemes: (i) Token-wise anatomy-guided contrastive learning, which aligns image tokens based on structural and categorical consistency to enhance fine-grained spatial-anatomical discrimination; (ii) Pixel-level anomaly-removal restoration, which particularly focuses on local anomalies, thereby refining the learned discrimination with detailed geometrical information. Additionally, we propose the Synthetic Lesion Mask to enhance anatomical diversity while preserving intra-consistency, which is typically corrupted by traditional data augmentations, such as Cropping and Affine transformations. Experimental results show that AFiRe: (i) provides robust anatomical discrimination, achieving more cohesive feature clusters compared to state-of-the-art contrastive learning methods; (ii) demonstrates superior generalization, surpassing 7 radiography-specific self-supervised methods in multi-label classification tasks with limited labeling; and (iii) integrates fine-grained information, enabling precise anomaly detection using only image-level annotations.

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IJCAI Conference 2025 Conference Paper

CFII-Net: Explicit Class Embeddings and Feature Maps Through Iterative Interaction for Boosting Medical Image Segmentation

Xinyu Zhu
Xiwen Liu
Lianghua He
Yin Wen

Prior knowledge of category structure is essential in medical image segmentation, especially with significant organ structure differences. However, current hybrid architectures primarily focus on enhancing pixel-level representation learning, often neglecting or weakening the key prior knowledge of categorical structures, which poses challenges in capturing category relationships and accurate segmenting. To address this concern, we propose a novel network using Explicit Class Embeddings and Feature Maps through Iterative Interaction (CFII-Net) for boosting medical image segmentation. CFII-Net effectively segments images by exploring the relationship between explicit class embeddings and pixels in images. Specifically, we propose an Explicit Class Embedding Generator (ECEG) to obtain high-quality class semantic embeddings, incorporating category structure priors, which are used to guide high-accuracy segmentation. We then introduce an iterative Interactor, which utilizes transformers to facilitate the interaction between feature maps and class embeddings, thereby exploring pixel-to-class relationships. Furthermore, we propose updating strategies to refine the class embeddings and feature maps during the iteration process for achieving refined image segmentation. Extensive empirical evidence shows that any codec can be easily integrated into CFII-Net and yields improvements over the state-of-the-art methods in four public benchmarks.

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AAAI Conference 2025 Conference Paper

Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge Transfer

Xinyue Chen
Miaojing Shi
Zijian Zhou
Lianghua He
Sophia Tsoka

Generalized few-shot semantic segmentation (GFSS) aims to segment objects of both base and novel classes, using sufficient samples of base classes and few samples of novel classes. Representative GFSS approaches typically employ a two-phase training scheme, involving base class pre-training followed by novel class fine-tuning, to learn the classifiers for base and novel classes respectively. Nevertheless, distribution gap exists between base and novel classes in this process. To narrow this gap, we exploit effective knowledge transfer from base to novel classes. First, a novel prototype modulation module is designed to modulate novel class prototypes by exploiting the correlations between base and novel classes. Second, a novel classifier calibration module is proposed to calibrate the weight distribution of the novel classifier according to that of the base classifier. Furthermore, existing GFSS approaches suffer from a lack of contextual information for novel classes due to their limited samples, we thereby introduce a context consistency learning scheme to transfer the contextual knowledge from base to novel classes. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our approach significantly enhances the state of the art in the GFSS setting.

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JBHI Journal 2025 Journal Article

FeaInfNet: Diagnosis of Medical Images With Feature-Driven Inference and Visual Explanations

Yitao Peng
Lianghua He
Die Hu
Yihang Liu
Longzhen Yang
Shaohua Shang

Interpretable deep-learning models have received widespread attention in the field of image recognition. However, owing to the coexistence of medical-image categories and the challenge of identifying subtle decision-making regions, many proposed interpretable deep-learning models suffer from insufficient accuracy and interpretability in diagnosing images of medical diseases. Therefore, this study proposed a feature-driven inference network (FeaInfNet) that incorporates a feature-based network reasoning structure. Specifically, local feature masks (LFM) were developed to extract feature vectors, thereby providing global information for these vectors and enhancing the expressive ability of FeaInfNet. Second, FeaInfNet compares the similarity of the feature vector corresponding to each subregion image patch with the disease and normal prototype templates that may appear in the region. It then combines the comparison of each subregion when making the final diagnosis. This strategy simulates the diagnosis process of doctors, making the model interpretable during the reasoning process, while avoiding misleading results caused by the participation of normal areas during reasoning. Finally, we proposed adaptive dynamic masks (Adaptive-DM) to interpret feature vectors and prototypes into human-understandable image patches to provide an accurate visual interpretation. Extensive experiments on multiple publicly available medical datasets, including RSNA, iChallenge-PM, COVID-19, ChinaCXRSet, MontgomerySet, and CBIS-DDSM, demonstrated that our method achieves state-of-the-art classification accuracy and interpretability compared with baseline methods in the diagnosis of medical images. Additional ablation studies were performed to verify the effectiveness of each component.

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AAAI Conference 2025 Conference Paper

M²RL-Net: Multi-View and Multi-Level Relation Learning Network for Weakly-Supervised Image Forgery Detection

Jiafeng Li
Ying Wen
Lianghua He

As digital media manipulation becomes increasingly sophisticated, accurately detecting and localizing image forgeries with minimal supervision has become a critical challenge. Existing weakly supervised image forgery detection (W-IFD) methods often rely on convolutional neural networks (CNNs) and limited exploration of internal relationships, leading to poor detection and localization performance with only image-level labels. To address these limitations, we introduce a novel Multi-View and Multi-Level Relation Learning Network (M²RL-Net) for W-IFD. M²RL-Net effectively identifies forged images using only image-level annotations by exploring relationships between different views and hierarchical levels within images. Specifically, M²RL-Net achieves patch-level self-consistency learning (PSL) and feature-level contrastive learning (FCL) across different views, facilitating more generalized self-supervised learning of forgery features. In detail, PSL employs self-supervised learning to distinguish consistent and inconsistent regions within images, enhancing its ability to accurately locate tampered areas. FCL utilizes feature-level self-view and multi-view contrastive learning to differentiate between genuine and tampered image features, thereby improving the recognition of authentic and manipulated content across different views. Extensive experiments on various datasets demonstrate that M²RL-Net outperforms existing weakly-supervised methods in both detection and localization accuracy. This research sets a new benchmark for weakly-supervised image forgery detection and lays a robust foundation for future studies in this field.

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JBHI Journal 2025 Journal Article

SFM-Net: Semantic Feature-Based Multi-Stage Network for Unsupervised Image Registration

Tai Ma
Xinru Dai
Suwei Zhang
Haidong Zou
Lianghua He
Ying Wen

It is difficult for general registration methods to establish the fine correspondence between images with complex anatomical structures. To overcome the above problem, this work presents SFM-Net, an unsupervised multi-stage semantic feature-based network. In addition to using the pixel-based similarity metrics, we propose a feature operator and emphasize a feature registration to improve the alignment of semantic related areas. Specifically, we design a two-stage training strategy, the intensity image registration stage and the semantic feature registration stage. The former is for valid semantic features learning and intensity-based coarse registration, while the latter is for semantic areas alignment, achieving fine transformation of anatomical structure. The same structure of both stages is composed of a dual-stream feature extraction module (DFEM) and a refined deformation field generation module (RDGM). Unlike the deep learning-based approaches that utilizing down-sampled encoder to extract features, DFEM constructed by dual-stream U-Net structure can capture semantic information in decoder feature for structural alignment. Different with approaches applying cascaded networks to learn deformation field, our proposed RDGM generates multi-scale deformation fields by performing a coarse-to-fine registration within a single network. Experiments on 3D brain MRI and liver CT datasets confirm that the proposed SFM-Net achieves accurate and diffeomorphic registration results, outperforming other state-of-the-art methods.

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UAI Conference 2023 Conference Paper

MMEL: A Joint Learning Framework for Multi-Mention Entity Linking

Chengmei Yang
Bowei He
Yimeng Wu
Chao Xing
Lianghua He
Chen Ma 0001

Entity linking, bridging mentions in the contexts with their corresponding entities in the knowledge bases, has attracted wide attention due to many potential applications. Recently, plenty of multimodal entity linking approaches have been proposed to take full advantage of the visual information rather than solely the textual modality. Although feasible, these methods mainly focus on the single-mention scenarios and neglect the scenarios where multiple mentions exist simultaneously in the same context, which limits the performance. In fact, such multi-mention scenarios are pretty common in public datasets and real-world applications. To solve this challenge, we first propose a joint feature extraction module to learn the representations of context and entity candidates, from both the visual and textual perspectives. Then, we design a pairwise training scheme (for training) and a multi-mention collaborative ranking method (for testing) to model the potential connections between different mentions. We evaluate our method on a public dataset and a self-constructed dataset, NYTimes-MEL, under both text-only and multimodal scenarios. The experimental results demonstrate that our method can largely outperform the state-of-the-art methods, especially in multi-mention scenarios. Our dataset and source code are publicly available at https: //github. com/ycm094/MMEL-main.

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AAAI Conference 2020 Conference Paper

Segmenting Medical MRI via Recurrent Decoding Cell

Ying Wen
Kai Xie
Lianghua He

The encoder-decoder networks are commonly used in medical image segmentation due to their remarkable performance in hierarchical feature fusion. However, the expanding path for feature decoding and spatial recovery does not consider the long-term dependency when fusing feature maps from different layers, and the universal encoder-decoder network does not make full use of the multi-modality information to improve the network robustness especially for segmenting medical MRI. In this paper, we propose a novel feature fusion unit called Recurrent Decoding Cell (RDC) which leverages convolutional RNNs to memorize the long-term context information from the previous layers in the decoding phase. An encoder-decoder network, named Convolutional Recurrent Decoding Network (CRDN), is also proposed based on RDC for segmenting multi-modality medical MRI. CRDN adopts CNN backbone to encode image features and decode them hierarchically through a chain of RDCs to obtain the ﬁnal high-resolution score map. The evaluation experiments on BrainWeb, MRBrainS and HVSMR datasets demonstrate that the introduction of RDC effectively improves the segmentation accuracy as well as reduces the model size, and the proposed CRDN owns its robustness to image noise and intensity non-uniformity in medical MRI.

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